Theoretical and Applied Climatology

, Volume 114, Issue 3–4, pp 511–529 | Cite as

Comparing precipitation bias correction methods for high-resolution regional climate simulations using COSMO-CLM

Effects on extreme values and climate change signal
Original Paper


A new parametric bias correction method for precipitation with an extension for extreme values is compared to an empirical and an existing parametric method. The bias corrections are applied to the regional climate model COSMO-CLM (consortium for small-scale modelling – climate limited area modelling) with a resolution of 4.5 km for the time periods 1991–2000 and 2091–2100. In addition to a comparison in a cross-validation framework, a focus is laid on the investigation of extreme value correction and the effect of the bias correction on the climate change signal. According to the statistical methods used in this study, it was found that the empirical method outperforms both parametric alternatives. However, due to the limited length of the available time series, some outliers occurred, and all methods had problems correcting extreme values. The climate change signal is moderately influenced by all three methods, and the power of climate change detection is reduced. The largest effect was found for the number of dry days and the mean daily intensity, which are considerably altered after correction.


Regional Climate Model Bias Correction Interpolation Scheme Skill Score General Pareto Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This study was funded by the State of Rhineland-Palatinate (Research Initiative Rhineland-Palatinate) and carried out within the “Global Change” project. The authors wish to thank the State Office for Environment, Water Management and Trade Control (LUWG) Rhineland-Palatinate at Mainz for providing the data of precipitation, the German Weather Service (DWD) for the REGNIE data set and the DKRZ for providing computing time. We also thank Burkhardt Rockel (HZG Geesthacht), Andreas Will (BTU Cottbus) and Hans-Jürgen Panitz (KIT Karlsruhe) for helping with the CCLM configuration, and we thank the CCLM Community. Additionally, we thank Lukas Schefczyk (University of Trier) for performing parts of the model runs and Philipp Reiter (RLP Kompetenzzentrum fr Klimawandelfolgen) for critical comments.


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Copyright information

© Springer-Verlag Wien 2013

Authors and Affiliations

  1. 1.University of TrierTrierGermany

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